Work

Enhancing Fuel Cell Hybrid Electric Vehicle Energy Management with Real-Time LSTM Speed Prediction

Year: 2025

Type: article

Abstract: This paper presents an innovative approach to optimize real-time energy management in fuel cell electric vehicles (FCEVs) through an integrated EMS (iEMS) framework based on a nested concept. Central to our method are two LSTM-based speed prediction models, trained and validated on open-source datasets to enhance adaptability and efficiency. The first model, trained on a 27 h real-time database, is embedded within the iEMS for dynamic real-time operation. The second model assesses the impact of incorporating external traffic data on the prediction accuracy, offering a systematic approach to refining speed prediction models. The results demonstrate significant improvements in fuel efficiency and overall performance compared to existing models. This study highlights the promise of data-driven AI models in next-generation FCEV energy management, contributing to smarter and more sustainable mobility solutions.

Source: Energies

DOI: 10.3390/en18164340

Authors: Matthieu Matignon, Mehdi Mcharek, Toufik Azib, Ahmed Chaibet

Institutions: École Supérieure des Techniques Aéronautiques et de Construction Automobile, Université de Bourgogne, Département de Recherche en Ingénierie des Véhicules pour l'Environnement

Language: English

Topic: Electric and Hybrid Vehicle Technologies

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $3,137